A case study of polar cap sporadic-E layer associated with TEC variations

The Sporadic-E (Es) layer is an often-observed phenomenon at high latitudes; however, our understanding of the polar cap Es layer is severely limited due to the scarce number of measurements. Here, the first comprehensive study of the polar cap Es layer associated with Global Positioning System (GPS) Total Electron Content (TEC) variations and scintillations is presented with multiple measurements at Resolute, Canada (Canadian Advanced Digital Ionosonde (CADI), Northward-looking face of Resolute Incoherent-Scatter Radar (RISR-N), and GPS receiver). According to the joint observations, the polar cap Es layer is a thin patch structure with variously high electron density, which gradually develops into the lower E region (~100 km) and horizontally extends >200 km. Moreover, the TEC variations produced by the polar cap Es layer are pulse-like enhancements with a general amplitude of ~0.5 TECu and are followed by smaller but rapid TEC perturbations. Furthermore, the possible scintillation effects likely associated with the polar cap Es layer are also discussed. As a consequence, the results widely expand our understanding on the polar cap Es layer, in particular on TEC variations

Validating the performance of the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) with in situ observations from DMSP and CHAMP

The Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) is a new empirical model of high latitude ionospheric electron density. While the introductory studies regarding E-CHAIM include validations, E-CHAIM’s topside model was notably excluded from independent validation using datasets not included in the model fitting. In this study, we undertake such a validation using in situ electron density observations from the Defense Meteorological Satellite Program (DMSP) constellation of satellites and the Challenging Mini-satellite Payload (CHAMP) mission. Through this validation, we show that E-CHAIM generally outperforms the International Reference Ionosphere (IRI) at DMSP orbit (~830 km altitude), with RMS errors of 8.3–9.8 × 109 e/m3 versus the IRI’s 1.2–1.3 × 1010 e/m3. E-CHAIM’s improvement over the IRI is consistent at all latitudes but is particularly noted in sub-auroral regions and is mainly limited to summer and equinox periods. At CHAMP orbit, E-CHAIM and the IRI are found to perform largely comparably, with E-CHAIM outperforming the IRI only marginally with RMS errors of 7.11 × 1010 e/m3 versus the IRI’s 7.48 × 1010 e/m3. This improvement is found to be largely constrained to sub-auroral latitudes with both models performing comparably at higher latitudes. An observed tendency for the IRI to overestimate electron density in the near-peak (at CHAMP orbit) and underestimate electron density at higher altitudes (DMSP orbit) appears to be consistent with previous work, which identified this pattern to result from shortcomings in the NeQuick topside function curvature at high latitudes

NG21A-05 Real-time resolution of instrumental biases using Rao-Blackwellized Particle Filtering

Particle filters are a non-linear data assimilation technique which use an ensemble of states to approximate the posterior density of the modelled geophysical system. While particle filters have nearly unparalleled flexibility to handle non-linear and non-Gaussian measurements, states, and errors, this comes at a cost of require comparatively large ensemble sizes when compared to other ensemble techniques. When the instruments which provide data to the model are subject to biases or calibration errors, each additional bias to be corrected adds another dimension to the state space, reducing the sampling efficiency of the entire ensemble. As the maximum ensemble size is generally limited by computational cost, this can create the perverse situation where adding additional observations decreases the performance of the data assimilation. Rao-Blackwellized particle filtering presents an efficient solution to this problem, allowing for the analytical solution of a conditionally linear Gaussian subset of the state space with minimal computational cost. This technique has been demonstrated successfully in two operational particle filter data assimilation models, the regional Assimilative Canadian High Arctic Ionospheric Model (A-CHAIM) and the global, real-time ionosphere/plasmasphere model Advanced Ionospheric Data Assimilation (AIDA). These models rely on the thousands of Global Navigation Satellite System (GNSS) receivers which provide an integrated measurement of electron density, but are subject to time-varying hardware-specific biases. The biases produced by these models will be compared to independently-derived values, and the improvement in model performance will be demonstrated

NG21A-05 Real-time resolution of instrumental biases using Rao-Blackwellized Particle Filtering

Particle filters are a non-linear data assimilation technique which use an ensemble of states to approximate the posterior density of the modelled geophysical system. While particle filters have nearly unparalleled flexibility to handle non-linear and non-Gaussian measurements, states, and errors, this comes at a cost of require comparatively large ensemble sizes when compared to other ensemble techniques. When the instruments which provide data to the model are subject to biases or calibration errors, each additional bias to be corrected adds another dimension to the state space, reducing the sampling efficiency of the entire ensemble. As the maximum ensemble size is generally limited by computational cost, this can create the perverse situation where adding additional observations decreases the performance of the data assimilation. Rao-Blackwellized particle filtering presents an efficient solution to this problem, allowing for the analytical solution of a conditionally linear Gaussian subset of the state space with minimal computational cost. This technique has been demonstrated successfully in two operational particle filter data assimilation models, the regional Assimilative Canadian High Arctic Ionospheric Model (A-CHAIM) and the global, real-time ionosphere/plasmasphere model Advanced Ionospheric Data Assimilation (AIDA). These models rely on the thousands of Global Navigation Satellite System (GNSS) receivers which provide an integrated measurement of electron density, but are subject to time-varying hardware-specific biases. The biases produced by these models will be compared to independently-derived values, and the improvement in model performance will be demonstrated